Why Trust in Data is Hard to Earn

📌 Data Governance 101 for BI Teams (How to Build Trust Without the Bureaucracy) Most companies don’t need an enterprise-grade data governance policy with 50 pages of rules and acronyms no one will ever read. They just need one thing: trust in their dashboards. Because the real problem isn’t the lack of data. It’s usually the lack of trust in it. And part of that confusion starts with the term itself. Data Governance is usually a vague phrase thrown around in meetings and strategy decks. Ask 10 people what it means, and you’ll get 12 different answers. Some think it’s about compliance. Others think it’s about permissions. And a few just assume it’s something IT should "handle." But at its core, governance isn’t about bureaucracy or control. It’s about clarity: → Knowing who owns what → How it’s defined → And whether it can be trusted when it matters most. You see this pattern everywhere. A marketing dashboard shows "Revenue" that doesn’t match what Finance is reporting. Sales metrics look inflated because duplicates slipped through the CRM. Operations teams export data manually just to double-check if Power BI is "right." And before anyone notices, confidence starts to fade. It’s a governance gap. And the good news? It doesn’t have to be complicated with endless documentation. It can be lean and practical but still effective. 1️⃣ 𝐃𝐞𝐟𝐢𝐧𝐞 𝐎𝐰𝐧𝐞𝐫𝐬𝐡𝐢𝐩 Start by assigning clear owners for each data domain. When something breaks, you know exactly who’s responsible for fixing it. When KPIs need to be updated, you know who makes the call. 2️⃣ 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐳𝐞 𝐃𝐞𝐟𝐢𝐧𝐢𝐭𝐢𝐨𝐧𝐬 This one might sound boring, but it’s the most underrated. If everyone defines KPIs differently, nothing else matters. When teams work from shared definitions, alignment happens naturally. You spend less time debating numbers and more time using them. Start simple. Keep a shared file, often called a Data Dictionary, listing each metric and its business definition. It doesn’t have to be perfect. It just needs to exist. 3️⃣ 𝐂𝐨𝐧𝐭𝐫𝐨𝐥 𝐀𝐜𝐜𝐞𝐬𝐬 Not everyone needs to see everything. That doesn’t mean you should hide data. It means you should curate it. Whether it’s for executives, managers, analysts, etc. A few clear access groups can reduce confusion and protect data integrity. Too much visibility without context can be just as dangerous as too little. 4️⃣ 𝐌𝐨𝐧𝐢𝐭𝐨𝐫 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 This is where trust is built or lost. If your dashboards show wrong numbers even once, users will remember it. It’s like credibility. You only get one chance. But it doesn’t have to be complicated. Start small: → Monitor refresh failures. → Detect duplicates. → Validate key fields like IDs or categories. These simple checks catch small issues before they break trust. And that’s how confidence in data slowly grows. If you get these four steps right, you’ll already be ahead of 90% of companies trying to become “data-driven.”

Stop blaming the data. Start building organizations where truth can survive contact with power. When analytics projects crash, it's easy to blame the data. "It's incomplete!" "It's biased!" "It's outdated!" There's often some truth in this, but just as often, it’s also a convenient fiction that protects egos while the real problems fester beneath the surface. Let's get real: Your data isn't always the villain. Your approach to data is just as likely to blame. Most organizations suffer from a form of "collection addiction"—hoarding terabytes of information while having no coherent plan for using it. They track everything from website clicks to coffee consumption patterns, then wonder why transformative insights don't magically appear. When you collect everything but question nothing, you've built a digital landfill, not a strategic asset. Meanwhile, the actual humans responsible for making sense of this information are drowning. Analysts get trapped creating beautiful dashboards for executives who focus more on the color scheme than changing their decision making. The result? Critical business decisions based on misinterpreted metrics and cherry-picked numbers that confirm the status quo. I have watched corporations make million-dollar decisions using data everyone privately acknowledged was being misread—but nobody would say so publicly. Sure, that's also a data quality problem. But the real issue here is a courage deficit. The hard truth? Many organizations still create hostile environments for what data actually provides: evidence that might challenge powerful people's assumptions. When an analyst's career prospects depend on delivering comfortable conclusions, don't be surprised when your "data-driven decisions" simply reinforce the status quo. In today's corporate landscape, the challenge isn’t dealing with bad data—it's dealing with inconvenient data. Fixing this requires more than better databases or fancier visualization tools. It demands creating environments where evidence matters more than hierarchy, where analytical skills are valued as much as technical ones, and where challenging questions are rewarded rather than silenced. Easy to say. Hard to incorporate into day-to-day culture. Stop blaming your data. Start building organizations where truth can survive contact with power. Because when it comes to analytics, your biggest competitive advantage isn't what you collect—it's what you're brave enough to hear.

Everyone loves the idea of data-driven decisions. → Clear dashboards → Insightful analysis → Smarter choices But very few talk about the hard part that happens before all of that. Here’s what it usually looks like behind the scenes: → Dig through dozens of systems to find the data → Realize no one owns the data or defines it the same way → Compare five sources of “truth” that don’t match → Spend hours fixing inconsistent formats, empty values, or odd naming conventions → Build custom rules to patch over logic gaps → Wait days for someone to approve access → Try to get a quick answer, but spend 80% of the time preparing the data → Finally get to analyze it, only to repeat the whole thing next month This isn’t a bug in the process. It is the process. Data work isn’t just analysis. It’s tracking lineage, aligning definitions, improving quality, and navigating politics. And that’s why the real value doesn’t come from just having data. It comes from having people who know how to make it usable. → They don’t just build dashboards → They build trust in the numbers behind them If you’re investing in analytics, make sure you’re investing in the plumbing too. It’s not the sexiest part of the job. But without it, nothing flows.

Years ago, I sent a forecast to the exec team that showed a clean runway into next year. Burn was steady. Pipeline looked strong. Everyone exhaled. Then, in the board meeting, someone asked a simple question: “Why does revenue drop off in November?” I hadn’t noticed. Neither had my analyst. Turns out a single formula was still pointing to an old source tab—with stale assumptions and broken links. The forecast was wrong. Not dramatically wrong. But wrong enough that trust took a hit. And rebuilding trust with your data? That’s expensive. It taught me two things that still shape how I model today: => 1. Bad data breaks more than your model. It breaks confidence. Momentum. Decision-making. Finance doesn’t get a lot of second chances—especially when the numbers are off. => 2. Every model needs a data audit, not just a review. Check your links. Trace your sources. Validate your assumptions. If your inputs are dirty, the whole model is compromised—no matter how clean it looks on the surface. Now I treat models like operating systems. They don’t just need updates—they need maintenance. And sometimes, a reboot. If you've ever gotten burned by a wrong number in the right cell, I see you. You're not alone. But it might be time to put process behind the polish.

This number is technically correct. So why doesn’t anyone trust it? This was one of the hardest lessons to learn early in my analytics career: Data accuracy ≠ data trust. You can build the cleanest model. You can double-check the SQL, audit the joins, QA the filters. And still… stakeholders say: “That number feels off.” “I don’t think that’s right.” “Let me check in Excel and get back to you.” Here’s what’s often really happening: 🔄 They don’t understand where the number is coming from. If they can’t trace it, they can’t trust it. Exposing calculation steps or using drill-throughs can help. 📊 The metric name isn’t aligned to what they think it means. You might call it Net Revenue. They think it’s Net Revenue after refunds. Boom, there is misalignment. 📆 They forgot the filters they asked for. “Why are we only looking at this year?” → “Because you asked for YTD only, remember?” Keep context visible. Always. 🧠 They’re comparing your number to what they expected, not what’s correct. And unfortunately, expectations are rarely documented. 🤝 You weren’t part of the business process that generates the data. So when something looks odd, they assume it’s a reporting issue, not a process or input issue. Here’s the kicker: Sometimes, being accurate isn’t enough. You also need to be understandable, explainable, and collaborative. That’s when trust happens. Have you ever been 100% confident in a metric, only to spend more time defending it than building it? #PowerBI #AnalyticsLife #DataTrust #DAX #SQL #DataQuality #DataStorytelling

I talk to a lot of finance and banking teams, and one thing keeps coming up: They’ve invested in data infrastructure, built data warehouses, even hired data teams but they still don’t trust their numbers. In almost every case, the problem isn’t the tooling. It’s the lack of data governance. There is no data ownership which leads to: - Risk pulls from inconsistent sources - Reports take weeks because teams are cleaning the same fields repeatedly - Reconciliations fail across systems - Decision-makers hesitate because they don’t trust what they see This can be resolved if your org doesn't view governance as additional paperwork. Governance gives us clear accountability defined quality standards, and traceability across systems. It helps us agree on what “clean” means, and build trust in the results before something goes wrong. In banking and finance, this is what gives us credibility. If your team still spends more time cleaning data than using it, it might be time to rethink how you govern it.

We keep talking about model accuracy. But the real currency in AI systems is trust. Not just “do I trust the model output?” But: • Do I trust the data pipeline that fed it? • Do I trust the agent’s behavior across edge cases? • Do I trust the humans who labeled the training data? • Do I trust the update cycle not to break downstream dependencies? • Do I trust the org to intervene when things go wrong? In the enterprise, trust isn’t a feeling. It’s a systems property. It lives in audit logs, versioning protocols, human-in-the-loop workflows, escalation playbooks, and update governance. But here’s the challenge: Most AI systems today don’t earn trust. They borrow it. They inherit it from the badge of a brand, the gloss of a UI, the silence of users who don’t know how to question a prediction. Until trust fails. • When the AI outputs toxic content. • When an autonomous agent nukes an inbox or ignores a critical SLA. • When a board discovers that explainability was just a PowerPoint slide. Then you realize: Trust wasn’t designed into the system. It was implied. Assumed. Deferred. Good AI engineering isn’t just about “shipping the model.” It’s about engineering trust boundaries that don’t collapse under pressure. And that means: → Failover, not just fine-tuning. → Safeguards, not just sandboxing. → Explainability that holds up in court, not just demos. → Escalation paths designed like critical infrastructure, not Jira tickets. We don’t need to fear AI. We need to design for trust like we’re designing for failure. Because we are. Where are you seeing trust gaps in your AI stack today? Let’s move the conversation beyond prompts and toward architecture.

"We had the data. We just didn’t trust it.” I’ve lost count of how many times I’ve heard that from a business leader mid-transformation. They had the tools. They had the talent. But when it came time to make a decision, no one could agree on which number was right. This is the quiet cost of misaligned governance. It doesn’t show up as a headline. It shows up in delays, rework, risk escalations, and second-guessing. If your teams can’t answer “where did this data come from?” or “who changed it last?” - then trust breaks down fast. That’s why I’m such a strong believer that governance isn’t a tech initiative. It’s a trust initiative. And trust is what gives business users the confidence to move.

If You Can't Trust Your Data, You Can't Trust Your Decisions. 𝗣𝗼𝗼𝗿 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗶𝘀 𝗺𝗼𝗿𝗲 𝗰𝗼𝗺𝗺𝗼𝗻 𝘁𝗵𝗮𝗻 𝘄𝗲 𝘁𝗵𝗶𝗻𝗸—𝗮𝗻𝗱 𝗶𝘁 𝗰𝗮𝗻 𝗯𝗲 𝗰𝗼𝘀𝘁𝗹𝘆. Yet, many businesses don't realise the damage until too late. 🔴 𝗙𝗹𝗮𝘄𝗲𝗱 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗿𝗲𝗽𝗼𝗿𝘁𝘀? Expect dire forecasts and wasted budgets. 🔴 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗿𝗲𝗰𝗼𝗿𝗱𝘀? Say goodbye to personalisation and marketing ROI. 🔴 𝗜𝗻𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝘀𝘂𝗽𝗽𝗹𝘆 𝗰𝗵𝗮𝗶𝗻 𝗱𝗮𝘁𝗮? Prepare for delays, inefficiencies, and lost revenue. 𝘗𝘰𝘰𝘳 𝘥𝘢𝘵𝘢 𝘲𝘶𝘢𝘭𝘪𝘵𝘺 𝘪𝘴𝘯'𝘵 𝘫𝘶𝘴𝘵 𝘢𝘯 𝘐𝘛 𝘪𝘴𝘴𝘶𝘦—𝘪𝘵'𝘴 𝘢 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘱𝘳𝘰𝘣𝘭𝘦𝘮. ❯ 𝑻𝒉𝒆 𝑺𝒊𝒙 𝑫𝒊𝒎𝒆𝒏𝒔𝒊𝒐𝒏𝒔 𝒐𝒇 𝑫𝒂𝒕𝒂 𝑸𝒖𝒂𝒍𝒊𝒕𝒚 To drive real impact, businesses must ensure their data is: ✓ 𝗔𝗰𝗰𝘂𝗿𝗮𝘁𝗲 – Reflects reality to prevent bad decisions. ✓ 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 – No missing values that disrupt operations. ✓ 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 – Uniform across systems for reliable insights. ✓ 𝗧𝗶𝗺𝗲𝗹𝘆 – Up to date when you need it most. ✓ 𝗩𝗮𝗹𝗶𝗱 – Follows required formats, reducing compliance risks. ✓ 𝗨𝗻𝗶𝗾𝘂𝗲 – No duplicates or redundant records that waste resources. ❯ 𝑯𝒐𝒘 𝒕𝒐 𝑻𝒖𝒓𝒏 𝑫𝒂𝒕𝒂 𝑸𝒖𝒂𝒍𝒊𝒕𝒚 𝒊𝒏𝒕𝒐 𝒂 𝑪𝒐𝒎𝒑𝒆𝒕𝒊𝒕𝒊𝒗𝒆 𝑨𝒅𝒗𝒂𝒏𝒕𝒂𝒈𝒆 Rather than fixing insufficient data after the fact, organisations must 𝗽𝗿𝗲𝘃𝗲𝗻𝘁 it: ✓ 𝗠𝗮𝗸𝗲 𝗘𝘃𝗲𝗿𝘆 𝗧𝗲𝗮𝗺 𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗹𝗲 – Data quality isn't just IT's job. ✓ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 – Proactive monitoring and correction reduce costly errors. ✓ 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘀𝗲 𝗗𝗮𝘁𝗮 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 – Identify issues before they impact operations. ✓ 𝗧𝗶𝗲 𝗗𝗮𝘁𝗮 𝘁𝗼 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗢𝘂𝘁𝗰𝗼𝗺𝗲𝘀 – Measure the impact on revenue, cost, and risk. ✓ 𝗘𝗺𝗯𝗲𝗱 𝗮 𝗖𝘂𝗹𝘁𝘂𝗿𝗲 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗘𝘅𝗰𝗲𝗹𝗹𝗲𝗻𝗰𝗲 – Treat quality as a mindset, not a project. ❯ 𝑯𝒐𝒘 𝑫𝒐 𝒀𝒐𝒖 𝑴𝒆𝒂𝒔𝒖𝒓𝒆 𝑺𝒖𝒄𝒄𝒆𝒔𝒔? The true test of data quality lies in outcomes: ✓ 𝗙𝗲𝘄𝗲𝗿 𝗲𝗿𝗿𝗼𝗿𝘀 → Higher operational efficiency ✓ 𝗙𝗮𝘀𝘁𝗲𝗿 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 → Reduced delays and disruptions ✓ 𝗟𝗼𝘄𝗲𝗿 𝗰𝗼𝘀𝘁𝘀 → Savings from automated data quality checks ✓ 𝗛𝗮𝗽𝗽𝗶𝗲𝗿 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 → Higher CSAT & NPS scores ✓ 𝗦𝘁𝗿𝗼𝗻𝗴𝗲𝗿 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 → Lower regulatory risks 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗱𝗮𝘁𝗮 𝗱𝗿𝗶𝘃𝗲𝘀 𝗯𝗲𝘁𝘁𝗲𝗿 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀. 𝗣𝗼𝗼𝗿 𝗱𝗮𝘁𝗮 𝗱𝗲𝘀𝘁𝗿𝗼𝘆𝘀 𝘁𝗵𝗲𝗺.

Data trust often drops as data quality improves. Sounds backwards, right? When I first started working with data and AI at large enterprises more than a decade ago, one of the biggest blockers for data and AI adoption was the lack of trust in data. That is what got me into the world of data quality in the first place. But here is what I did not expect: 👉 As companies became more data mature and improved their data foundations (including data quality), data trust across the organisation often dropped. Should it not be the other way around? You would think that better data and increased data maturity would increase data trust. The reality is: 👉 Trust in data is not just about data quality. It is about whether the data, and its quality, meet the expectations and requirements for the data and AI use cases. When organisations become more data mature, their use cases evolve. Typically, it can look like this: 1️⃣ Ad hoc analytics 2️⃣ Dashboards used by management 3️⃣ Data products 4️⃣ Data as a product 5️⃣ AI/ML in production Advanced data use cases such as data products and AI/ML in production require much higher data quality than the "simpler" use cases. And here is the big problem: the data quality requirement increases much faster than the speed at which the underlying data quality improves. That is why organisational trust in data decreases when data maturity increases, even though the underlying data quality actually improves. 👉 For data leaders, here is the takeaway: To come out on top, you have to take data quality extremely seriously and proactively. Much more so than what is happening at the average enterprise out there right now. Data quality cannot be an afterthought. Do you agree?